Overview

SparkR is an R package that provides a light-weight frontend to use Apache Spark from R.
In Spark 2.1.2, SparkR provides a distributed data frame implementation that
supports operations like selection, filtering, aggregation etc. (similar to R data frames,
dplyr) but on large datasets. SparkR also supports distributed
machine learning using MLlib.

SparkDataFrame

A SparkDataFrame is a distributed collection of data organized into named columns. It is conceptually
equivalent to a table in a relational database or a data frame in R, but with richer
optimizations under the hood. SparkDataFrames can be constructed from a wide array of sources such as:
structured data files, tables in Hive, external databases, or existing local R data frames.

All of the examples on this page use sample data included in R or the Spark distribution and can be run using the ./bin/sparkR shell.

Starting Up: SparkSession

The entry point into SparkR is the SparkSession which connects your R program to a Spark cluster.
You can create a SparkSession using sparkR.session and pass in options such as the application name, any spark packages depended on, etc. Further, you can also work with SparkDataFrames via SparkSession. If you are working from the sparkR shell, the SparkSession should already be created for you, and you would not need to call sparkR.session.

sparkR.session()

Starting Up from RStudio

You can also start SparkR from RStudio. You can connect your R program to a Spark cluster from
RStudio, R shell, Rscript or other R IDEs. To start, make sure SPARK_HOME is set in environment
(you can check Sys.getenv),
load the SparkR package, and call sparkR.session as below. It will check for the Spark installation, and, if not found, it will be downloaded and cached automatically. Alternatively, you can also run install.spark manually.

In addition to calling sparkR.session,
you could also specify certain Spark driver properties. Normally these
Application properties and
Runtime Environment cannot be set programmatically, as the
driver JVM process would have been started, in this case SparkR takes care of this for you. To set
them, pass them as you would other configuration properties in the sparkConfig argument to
sparkR.session().

The following Spark driver properties can be set in sparkConfig with sparkR.session from RStudio:

Property Name

Property group

spark-submit equivalent

spark.master

Application Properties

--master

spark.yarn.keytab

Application Properties

--keytab

spark.yarn.principal

Application Properties

--principal

spark.driver.memory

Application Properties

--driver-memory

spark.driver.extraClassPath

Runtime Environment

--driver-class-path

spark.driver.extraJavaOptions

Runtime Environment

--driver-java-options

spark.driver.extraLibraryPath

Runtime Environment

--driver-library-path

Creating SparkDataFrames

With a SparkSession, applications can create SparkDataFrames from a local R data frame, from a Hive table, or from other data sources.

From local data frames

The simplest way to create a data frame is to convert a local R data frame into a SparkDataFrame. Specifically we can use as.DataFrame or createDataFrame and pass in the local R data frame to create a SparkDataFrame. As an example, the following creates a SparkDataFrame based using the faithful dataset from R.

From Data Sources

SparkR supports operating on a variety of data sources through the SparkDataFrame interface. This section describes the general methods for loading and saving data using Data Sources. You can check the Spark SQL programming guide for more specific options that are available for the built-in data sources.

The general method for creating SparkDataFrames from data sources is read.df. This method takes in the path for the file to load and the type of data source, and the currently active SparkSession will be used automatically.
SparkR supports reading JSON, CSV and Parquet files natively, and through packages available from sources like Third Party Projects, you can find data source connectors for popular file formats like Avro. These packages can either be added by
specifying --packages with spark-submit or sparkR commands, or if initializing SparkSession with sparkPackages parameter when in an interactive R shell or from RStudio.

sparkR.session(sparkPackages ="com.databricks:spark-avro_2.11:3.0.0")

We can see how to use data sources using an example JSON input file. Note that the file that is used here is not a typical JSON file. Each line in the file must contain a separate, self-contained valid JSON object. For more information, please see JSON Lines text format, also called newline-delimited JSON. As a consequence, a regular multi-line JSON file will most often fail.

From Hive tables

You can also create SparkDataFrames from Hive tables. To do this we will need to create a SparkSession with Hive support which can access tables in the Hive MetaStore. Note that Spark should have been built with Hive support and more details can be found in the SQL programming guide. In SparkR, by default it will attempt to create a SparkSession with Hive support enabled (enableHiveSupport = TRUE).

SparkDataFrame Operations

SparkDataFrames support a number of functions to do structured data processing.
Here we include some basic examples and a complete list can be found in the API docs:

Selecting rows, columns

# Create the SparkDataFrame
df <- as.DataFrame(faithful)# Get basic information about the SparkDataFrame
df
## SparkDataFrame[eruptions:double, waiting:double]# Select only the "eruptions" columnhead(select(df, df$eruptions))## eruptions##1 3.600##2 1.800##3 3.333# You can also pass in column name as stringshead(select(df,"eruptions"))# Filter the SparkDataFrame to only retain rows with wait times shorter than 50 minshead(filter(df, df$waiting <50))## eruptions waiting##1 1.750 47##2 1.750 47##3 1.867 48

Grouping, Aggregation

SparkR data frames support a number of commonly used functions to aggregate data after grouping. For example we can compute a histogram of the waiting time in the faithful dataset as shown below

# We use the `n` operator to count the number of times each waiting time appearshead(summarize(groupBy(df, df$waiting), count = n(df$waiting)))## waiting count##1 70 4##2 67 1##3 69 2# We can also sort the output from the aggregation to get the most common waiting times
waiting_counts <- summarize(groupBy(df, df$waiting), count = n(df$waiting))head(arrange(waiting_counts, desc(waiting_counts$count)))## waiting count##1 78 15##2 83 14##3 81 13

Operating on Columns

SparkR also provides a number of functions that can directly applied to columns for data processing and during aggregation. The example below shows the use of basic arithmetic functions.

# Convert waiting time from hours to seconds.# Note that we can assign this to a new column in the same SparkDataFrame
df$waiting_secs <- df$waiting *60head(df)## eruptions waiting waiting_secs##1 3.600 79 4740##2 1.800 54 3240##3 3.333 74 4440

Applying User-Defined Function

In SparkR, we support several kinds of User-Defined Functions:

Run a given function on a large dataset using dapply or dapplyCollect

dapply

Apply a function to each partition of a SparkDataFrame. The function to be applied to each partition of the SparkDataFrame
and should have only one parameter, to which a data.frame corresponds to each partition will be passed. The output of function should be a data.frame. Schema specifies the row format of the resulting a SparkDataFrame. It must match to data types of returned value.

dapplyCollect

Like dapply, apply a function to each partition of a SparkDataFrame and collect the result back. The output of function
should be a data.frame. But, Schema is not required to be passed. Note that dapplyCollect can fail if the output of UDF run on all the partition cannot be pulled to the driver and fit in driver memory.

Run a given function on a large dataset grouping by input column(s) and using gapply or gapplyCollect

gapply

Apply a function to each group of a SparkDataFrame. The function is to be applied to each group of the SparkDataFrame and should have only two parameters: grouping key and R data.frame corresponding to
that key. The groups are chosen from SparkDataFrames column(s).
The output of function should be a data.frame. Schema specifies the row format of the resulting
SparkDataFrame. It must represent R function’s output schema on the basis of Spark data types. The column names of the returned data.frame are set by user.

gapplyCollect

Like gapply, applies a function to each partition of a SparkDataFrame and collect the result back to R data.frame. The output of the function should be a data.frame. But, the schema is not required to be passed. Note that gapplyCollect can fail if the output of UDF run on all the partition cannot be pulled to the driver and fit in driver memory.

Data type mapping between R and Spark

Run local R functions distributed using spark.lapply

spark.lapply

Similar to lapply in native R, spark.lapply runs a function over a list of elements and distributes the computations with Spark.
Applies a function in a manner that is similar to doParallel or lapply to elements of a list. The results of all the computations
should fit in a single machine. If that is not the case they can do something like df <- createDataFrame(list) and then use
dapply

Running SQL Queries from SparkR

A SparkDataFrame can also be registered as a temporary view in Spark SQL and that allows you to run SQL queries over its data.
The sql function enables applications to run SQL queries programmatically and returns the result as a SparkDataFrame.

# Load a JSON file
people <- read.df("./examples/src/main/resources/people.json","json")# Register this SparkDataFrame as a temporary view.
createOrReplaceTempView(people,"people")# SQL statements can be run by using the sql method
teenagers <- sql("SELECT name FROM people WHERE age >= 13 AND age <= 19")head(teenagers)## name##1 Justin

Collaborative Filtering

Statistics

Under the hood, SparkR uses MLlib to train the model. Please refer to the corresponding section of MLlib user guide for example code.
Users can call summary to print a summary of the fitted model, predict to make predictions on new data, and write.ml/read.ml to save/load fitted models.
SparkR supports a subset of the available R formula operators for model fitting, including ‘~’, ‘.’, ‘:’, ‘+’, and ‘-‘.

Since part of SparkR is modeled on the dplyr package, certain functions in SparkR share the same names with those in dplyr. Depending on the load order of the two packages, some functions from the package loaded first are masked by those in the package loaded after. In such case, prefix such calls with the package name, for instance, SparkR::cume_dist(x) or dplyr::cume_dist(x).

Migration Guide

Upgrading From SparkR 1.5.x to 1.6.x

Before Spark 1.6.0, the default mode for writes was append. It was changed in Spark 1.6.0 to error to match the Scala API.

SparkSQL converts NA in R to null and vice-versa.

Upgrading From SparkR 1.6.x to 2.0

The method table has been removed and replaced by tableToDF.

The class DataFrame has been renamed to SparkDataFrame to avoid name conflicts.

Spark’s SQLContext and HiveContext have been deprecated to be replaced by SparkSession. Instead of sparkR.init(), call sparkR.session() in its place to instantiate the SparkSession. Once that is done, that currently active SparkSession will be used for SparkDataFrame operations.

The parameter sparkExecutorEnv is not supported by sparkR.session. To set environment for the executors, set Spark config properties with the prefix “spark.executorEnv.VAR_NAME”, for example, “spark.executorEnv.PATH”